Training in realistic situations often yields substantial performance improvement. And like many activities, users of weapons typically become more proficient with increased experience. However, amassing high-value experience with weapons poses unique training challenges, as most real weapons can be inherently dangerous.
In one conventional weapons-based training exercise, a trainee uses a weapon model or real weapon to aim at a two-dimensional training scenario displayed on a video wall (such as a projection screen or large screen monitor). When firing the weapon, a beam of laser light projects onto the video wall, where a ‘hit location’ is picked up by a sensor, such as a camera trained on the screen where the trainee is aiming. The camera and its associated system measures accuracy of the shot. Due to the nature of this method, the system typically remains unaware of the location/aim of the weapon until the weapon is fired. Likewise, the location/position of the trainee is typically lost. A limitation of such conventional systems concerns realism, as the trainee generally remains un-immersed in the scenario.
Another conventional training method relies on using weapons in a shoot house, where the weapons either fire real ammunition, less-lethal rounds (sim-rounds of various kinds), or paint-balls. Rather than in simulation, training occurs in mock-up or abandoned houses or even complete villages. While such training can seem very real, costs of different scenarios is typically high, and modifying a scenario involves difficulties as hard-scapes need to be moved or created to change between one simulation and another. Additionally, using real ammunition is dangerous as well as expensive, as the mock-ups become damaged during training and have to be rebuilt periodically. While immersive in nature, such conventional training can be cost prohibitive, and, depending on the type of rounds used, can further be very dangerous, especially for group- and squad-level training.
In view of the foregoing discussion of representative shortcomings in the art, need for improved training technology, for weapons as well as other activities, is apparent. Need is further apparent for technology for capturing and recreating real-world scenarios, in training and other contexts. Need further exists for technology for tracking and providing information about physical objects utilized in training simulations, including how trainees are interacting with such objects and how objects may change or undergo state changes in connection with trainee interaction. Need further exists for improving information latency and/or transmission rates in motion capture environments. A capability addressing such need, or some other related deficiency in the art, would support enhancements in training, simulation, and motion capture, as well as other applications.